Journal article
Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm
Renewable Energy, Vol.135, pp.1412-1434
2019
Abstract
In this paper, the optimal design and energy management of the hybrid systems including the photovoltaic (PV) panels, wind turbine (WT) and fuel cell (FC) based on hydrogen storage (HS) (PWFHS) are presented to minimize the total net present cost (TNPC) of northwest region of Iran using intelligent flower pollination algorithm (FPA). The reliability indices that are considered simultaneously as technical constraints are the loss of energy expected (LOEE) and the loss of load expected (LOLE). The FPA performance is compared with well-known optimization methods such as teaching-learning based optimization (TLBO), particle swarm optimization (PSO) and also last researches in hybrid renewable energy designing. The simulation results are presented including decision variables, TNPC, LOEE, LOLE, energy management of generation units in different LOLEmax and LOEEmax and different combination of PWFHS. The results show that the proposed methodology finds the optimal decision variables easily with fast convergence, lower cost and better reliability values in different reliability indices and different PWFHS in comparison to TLBO and PSO.
Details
- Title
- Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm
- Authors
- Mohammad Jafar Hadidian Moghaddam (Author) - Victoria UniversityAkhtar Kalam (Author) - Victoria UniversitySaber Arabi Nowdeh (Author) - Electrical Department, Payambar'azam Student Research Center, Aq'qala, Golestan, IranAbdollah Ahmadi (Author) - UNSW AustraliaManoochehr Babanezhad (Author) - Golestan UniversitySajeeb Saha (Author) - Deakin University
- Publication details
- Renewable Energy, Vol.135, pp.1412-1434
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.renene.2018.09.078
- ISSN
- 1879-0682
- Organisation Unit
- School of Science, Technology and Engineering; University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99532307002621
- Output Type
- Journal article
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